Page 1

1C-D

UMAP projections show sample groups (left), cell type annotations (middle), and CHIKV+ cells (right).


The fraction of cells identified for each cell type is shown below.


1E

The number of differentially expressed genes identified for mock vs CHIKV is shown below for each cell type.


1F

Bargraphs showing top GSEA disease ontology terms for genes upregulated when comparing all mock vs CHIKV-infected macrophages. GSEA terms were filtered to remove broad terms that include >250 genes.

Enrichment is shown for select top GSEA terms



Page 2

2B

The fraction of CHIKV+ cells identified for each cell type is shown below.



Page 4

4A-B

UMAP projections show macrophage subsets.


4C

UMAP projections show CHIKV+ cells (left) and CHIKV-high clusters (middle, right) for CHIKV-infected samples.


4D

Heatmaps show expression of the top 20 genes upregulated in CHIKV- vs mock-infected samples for each macrophage subset. Genes are arranged by the number of subsets upregulating the gene and the maximum adjusted p-value for the replicates. Black diamonds mark the top 20 upregulated genes for each subset, grey diamonds mark other subsets where each gene is upregulated.



Page 5

5A

Expression of select genes upregulated in CHIKV- vs mock-infected samples is shown below for macrophage subsets.


5D

Heatmaps show expression of the top 10 marker genes for each macrophage subset. Genes are sorted by minimum fold change for the replicates.



Page 6

6A

Ifng expression is shown below for each cell type for CHIKV-infected samples. CD4 Teffs are plotted as a separate group.


6B

UMAP projections show T cell subsets.


6C

Heatmaps show expression of the top 15 genes upregulated in CHIKV- vs mock-infected samples for each T cell subset. Genes are arranged by the number of cell types upregulating the gene and the maximum adjusted p-value for the replicates. Black diamonds mark the top 15 upregulated genes for each subset, grey diamonds mark other subsets where each gene is upregulated.


6D

Ifng expression is shown below for each T cell subset.



Page 7

7A

T cell marker genes are shown below for mock- and CHIKV-infected samples.



Page 11

11A

Heatmaps show expression of the top 10 marker genes for each macrophage subset. Genes are sorted by minimum fold change for the replicates.



Session info

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                 
##  [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8       
##  [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8      
##  [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8          
##  [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8     
## [11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8
## 
## time zone: America/Denver
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] qs_0.25.7                   edgeR_4.0.12               
##  [3] limma_3.58.1                metap_1.9                  
##  [5] harmony_1.2.0               gprofiler2_0.2.2           
##  [7] presto_1.0.0                data.table_1.14.10         
##  [9] Rcpp_1.0.11                 M3Drop_1.28.0              
## [11] numDeriv_2016.8-1.1         DoubletFinder_2.0.3        
## [13] scuttle_1.12.0              SingleCellExperiment_1.24.0
## [15] SummarizedExperiment_1.32.0 GenomicRanges_1.54.1       
## [17] GenomeInfoDb_1.38.5         MatrixGenerics_1.14.0      
## [19] matrixStats_1.2.0           clustifyrdata_1.1.0        
## [21] clustifyr_1.14.0            SeuratObject_4.1.4         
## [23] Seurat_4.4.0                ggupset_0.3.0              
## [25] patchwork_1.1.3             ggrepel_0.9.4              
## [27] scales_1.3.0                djvdj_0.1.0                
## [29] colorblindr_0.1.0           colorspace_2.1-0           
## [31] ggtrace_0.2.0.9000          furrr_0.3.1                
## [33] future_1.33.1               xlsx_0.6.5                 
## [35] knitr_1.45                  cowplot_1.1.2              
## [37] here_1.0.1                  broom_1.0.5                
## [39] lubridate_1.9.3             forcats_1.0.0              
## [41] stringr_1.5.1               dplyr_1.1.4                
## [43] purrr_1.0.2                 readr_2.1.4                
## [45] tidyr_1.3.0                 tibble_3.2.1               
## [47] ggplot2_3.4.4               tidyverse_2.0.0            
## [49] org.Hs.eg.db_3.18.0         org.Mm.eg.db_3.18.0        
## [51] AnnotationDbi_1.64.1        IRanges_2.36.0             
## [53] S4Vectors_0.40.2            Biobase_2.62.0             
## [55] BiocGenerics_0.48.1         biomaRt_2.58.0             
## [57] DOSE_3.28.2                 msigdbr_7.5.1              
## [59] enrichplot_1.22.0           clusterProfiler_4.10.0     
## 
## loaded via a namespace (and not attached):
##   [1] vroom_1.6.5               progress_1.2.3           
##   [3] nnet_7.3-19               goftest_1.2-3            
##   [5] Biostrings_2.70.1         TH.data_1.1-2            
##   [7] rstan_2.32.3              vctrs_0.6.5              
##   [9] spatstat.random_3.2-2     RApiSerialize_0.1.2      
##  [11] digest_0.6.33             png_0.1-8                
##  [13] deldir_2.0-2              parallelly_1.36.0        
##  [15] MASS_7.3-60               reshape2_1.4.4           
##  [17] httpuv_1.6.13             qvalue_2.34.0            
##  [19] withr_2.5.2               xfun_0.41                
##  [21] ggfun_0.1.3               ellipsis_0.3.2           
##  [23] survival_3.5-7            memoise_2.0.1            
##  [25] gson_0.1.0                tidytree_0.4.6           
##  [27] zoo_1.8-12                gtools_3.9.5             
##  [29] pbapply_1.7-2             entropy_1.3.1            
##  [31] Formula_1.2-5             prettyunits_1.2.0        
##  [33] KEGGREST_1.42.0           promises_1.2.1           
##  [35] httr_1.4.7                globals_0.16.2           
##  [37] fitdistrplus_1.1-11       stringfish_0.16.0        
##  [39] rstudioapi_0.15.0         miniUI_0.1.1.1           
##  [41] generics_0.1.3            base64enc_0.1-3          
##  [43] babelgene_22.9            curl_5.2.0               
##  [45] zlibbioc_1.48.0           ggraph_2.1.0             
##  [47] TFisher_0.2.0             polyclip_1.10-6          
##  [49] GenomeInfoDbData_1.2.11   SparseArray_1.2.3        
##  [51] xtable_1.8-4              evaluate_0.23            
##  [53] S4Arrays_1.2.0            BiocFileCache_2.10.1     
##  [55] hms_1.1.3                 irlba_2.3.5.1            
##  [57] filelock_1.0.3            ROCR_1.0-11              
##  [59] reticulate_1.34.0         spatstat.data_3.0-3      
##  [61] magrittr_2.0.3            lmtest_0.9-40            
##  [63] later_1.3.2               viridis_0.6.4            
##  [65] ggtree_3.10.0             lattice_0.22-5           
##  [67] spatstat.geom_3.2-7       future.apply_1.11.1      
##  [69] scattermore_1.2           XML_3.99-0.16            
##  [71] shadowtext_0.1.2          RcppAnnoy_0.0.21         
##  [73] StanHeaders_2.26.28       Hmisc_5.1-1              
##  [75] pillar_1.9.0              nlme_3.1-164             
##  [77] caTools_1.18.2            compiler_4.3.1           
##  [79] beachmat_2.18.0           stringi_1.8.3            
##  [81] tensor_1.5                plyr_1.8.9               
##  [83] crayon_1.5.2              abind_1.4-5              
##  [85] gridGraphics_0.5-1        sn_2.1.1                 
##  [87] locfit_1.5-9.8            sp_2.1-2                 
##  [89] mathjaxr_1.6-0            graphlayouts_1.0.2       
##  [91] bit_4.0.5                 sandwich_3.1-0           
##  [93] fastmatch_1.1-4           multcomp_1.4-25          
##  [95] codetools_0.2-19          QuickJSR_1.0.9           
##  [97] bslib_0.6.1               plotly_4.10.3            
##  [99] multtest_2.58.0           mime_0.12                
## [101] splines_4.3.1             dbplyr_2.4.0             
## [103] sparseMatrixStats_1.14.0  HDO.db_0.99.1            
## [105] blob_1.2.4                utf8_1.2.4               
## [107] reldist_1.7-2             fs_1.6.3                 
## [109] checkmate_2.3.1           listenv_0.9.0            
## [111] DelayedMatrixStats_1.24.0 Rdpack_2.6               
## [113] pkgbuild_1.4.3            ggplotify_0.1.2          
## [115] Matrix_1.6-1.1            statmod_1.5.0            
## [117] tzdb_0.4.0                tweenr_2.0.2             
## [119] pkgconfig_2.0.3           tools_4.3.1              
## [121] cachem_1.0.8              rbibutils_2.2.16         
## [123] RSQLite_2.3.4             viridisLite_0.4.2        
## [125] DBI_1.2.0                 fastmap_1.1.1            
## [127] rmarkdown_2.25            grid_4.3.1               
## [129] ica_1.0-3                 sass_0.4.8               
## [131] dotCall64_1.1-1           RANN_2.6.1               
## [133] rpart_4.1.23              farver_2.1.1             
## [135] mgcv_1.9-1                tidygraph_1.3.0          
## [137] scatterpie_0.2.1          yaml_2.3.8               
## [139] foreign_0.8-86            cli_3.6.2                
## [141] leiden_0.4.3.1            lifecycle_1.0.4          
## [143] uwot_0.1.16               mvtnorm_1.2-4            
## [145] backports_1.4.1           BiocParallel_1.36.0      
## [147] timechange_0.2.0          gtable_0.3.4             
## [149] ggridges_0.5.5            densEstBayes_1.0-2.2     
## [151] progressr_0.14.0          parallel_4.3.1           
## [153] ape_5.7-1                 jsonlite_1.8.8           
## [155] bitops_1.0-7              bit64_4.0.5              
## [157] qqconf_1.3.2              Rtsne_0.17               
## [159] yulab.utils_0.1.2         spatstat.utils_3.0-4     
## [161] RcppParallel_5.1.7        mutoss_0.1-13            
## [163] bdsmatrix_1.3-6           highr_0.10               
## [165] jquerylib_0.1.4           loo_2.6.0                
## [167] GOSemSim_2.28.0           lazyeval_0.2.2           
## [169] shiny_1.8.0               htmltools_0.5.7          
## [171] rJava_1.0-10              GO.db_3.18.0             
## [173] sctransform_0.4.1         rappdirs_0.3.3           
## [175] glue_1.6.2                spam_2.10-0              
## [177] XVector_0.42.0            RCurl_1.98-1.13          
## [179] rprojroot_2.0.4           treeio_1.26.0            
## [181] mnormt_2.1.1              gridExtra_2.3            
## [183] igraph_1.6.0              R6_2.5.1                 
## [185] gplots_3.1.3              labeling_0.4.3           
## [187] xlsxjars_0.6.1            cluster_2.1.6            
## [189] bbmle_1.0.25.1            aplot_0.2.2              
## [191] plotrix_3.8-4             rstantools_2.3.1.1       
## [193] DelayedArray_0.28.0       tidyselect_1.2.0         
## [195] htmlTable_2.4.2           inline_0.3.19            
## [197] ggforce_0.4.1             xml2_1.3.6               
## [199] munsell_0.5.0             KernSmooth_2.23-22       
## [201] htmlwidgets_1.6.4         fgsea_1.28.0             
## [203] RColorBrewer_1.1-3        rlang_1.1.2              
## [205] spatstat.sparse_3.0-3     spatstat.explore_3.2-5   
## [207] fansi_1.0.6